(HTR): Unravelling the Black Box Rosmin Esmail, PhD Candidate Dr. - - PowerPoint PPT Presentation

Knowledge Translation (KT) and Health Technology Reassessment (HTR): Unravelling the Black Box

Rosmin Esmail, PhD Candidate

• Dr. Tom Stelfox
• Dr. Fiona Clement

University of Calgary April 15, 2019 CADTH Symposium 2019

Disclosure

We have no actual or potential conflict of interest in relation to this topic or presentation.

Low Value High Value

What is the problem?

Underused & highly beneficial, clinical- and cost-effective Overused/misused & unnecessary, NOT clinical- and/or cost-effective

Why is this a problem?

How big is the problem?

Health Technology Reassessment (HTR)

• Structured, evidence-based assessment of the medical,

economic, social and ethical impacts of a health technology (e.g., drug, device, test, procedure, etc.) currently used in the healthcare system, to inform its optimal use in comparison to its alternatives

(Noseworthy & Clement, 2012)

Conceptual model for HTR

(Soril et al., 2017)

Clarifying Terminology

Disinvestment: The processes of (partially or

completely) withdrawing health resources from currently funded areas that provide little benefit for their cost (Elshaug, 2009)

De-implementation: use of low-value care is

reduced or stopped in a planned process (van Bodegom-Vos, 2017)

De-adoption: discontinuation or rejection of a

clinical practice after it was previously adopted (Rogers, 2003).

Outcomes: achieving the change, not achieving the change, remaining at status quo Outputs: increased use or adoption, decreased use, no change, de-adoption of the technology

So how can we mobilize HTR outputs?

Field of KT KT has been used effectively to implement

new interventions into clinical practice

Can it be used for HTR?

Understanding Knowledge Translation

Terms for Knowledge Translation

KT has Many Names

 Knowledge management, knowledge mobilization, K*…  Also known as effectiveness research, patient oriented

research

 UK: implementation science or research utilization  US: dissemination, diffusion, research use, knowledge

transfer and uptake

 Canada: knowledge transfer and exchange, and knowledge

translation

CIHR definition (part 1) Knowledge translation is a dynamic and iterative process that includes the synthesis, dissemination, exchange & ethically sound application of knowledge to improve the health of Canadians, provide more effective health services and products, and strengthen the healthcare system.

CIHR definition, (part 2) This process takes place within a complex system of interactions between researchers and knowledge users which may vary in intensity, complexity and level of engagement depending on the nature of the research and the findings as well as the needs of the particular knowledge user (Graham, 2010).

Why Study it?

Mechanism for determining how the two are linked

How KT approaches can be used in the translation

• f HTR outputs

Leading to optimal care for patients Fewer wasted resources

Illuminate the understanding of the KE and

utilization function of HTR model

Esmail R et al, 2018 Knowledge translation and health technology reassessment: identifying synergy. BMC Health Services Research 201818:674https://doi.org/10.1186/s12913-018-3494-y

Barriers and Facilitators to KT in the context of HTR

WHO Category* Barriers Facilitators Climate and Context Physicians are reluctant to dismiss

• utmoded devices

Use of clinical champions Linkage and Exchange Lack of a well planned strategy for implementation that engages all stakeholders Broad and early stakeholder engagement Research Evidence, HTR process, resources/timelines Lack of relevant evidence of the technology itself Good evidence base for the identification and recommendations Role of Researchers and HTR Difficulty in communicating with a variety of audiences Capacity building in KT and change management Role of Stakeholders, Knowledge users, and the health system in HTR, skills and expertise Lack of resources and human resources to support HTR Decision makers need to understand the HTR process and provide support

*World Health Organization’s classification of barriers and facilitators, WHO, 2012)

PhD Research Question

Employing a health systems perspective, this project will study and determine how KT approaches are used to translate HTR outputs to achieve the desired

• utcomes?

Methodological Approach

Multiple methods

Systematic review of KT Theories, Models, Frameworks

(TMFs)

Modified Delphi Process for expert validation (underway) Key informant interviews

What is a Theory, Model, Framework?

Theory: a set of analytical principles or statements designed to structure our observation, understanding and explanation of the world Model: a deliberate simplification of a phenomenon or a specific aspect of a phenomenon Framework: usually denotes a structure, overview,

• utline, system or plan consisting of various descriptive

categories

Nilsen P , 2015

Many KT theories, models, frameworks

• Tabak et al (2012)-61 dissemination and implementation research theories, models,

and frameworks

• Locker et al (2015)-51 classification schemes (23 taxonomies, 15 frameworks, eight

intervention lists, three models and two other approaches) on KT interventions that could be used to integrate evidence into practice

• Milat et al (2017)-found 41 different frameworks and models from 98 papers with a

focus on research translation frameworks

• Strifler et al (2018)-limited to models, theories and frameworks used in cancer and

chronic disease management and prevention, searched 305 KT theories, models and frameworks, and identified 159 articles that met the inclusion criteria of the review

• Identified 26 full-spectrum KT theories, models, frameworks

Full-Spectrum TMF

Consist of all four KT phases:

 planning/design (identifies a knowledge gap,

engages stakeholders, develops an intervention),

 implementation,  evaluation, and  sustainability/scalability

Strifler et al, 2018

Esmail et al (unpublished, 2018)

Full-Spectrum KT Theories, Models, Frameworks

36 Full-Spectrum KT Theories, Models, Frameworks

Internal Committee Review of 36 TMFs

 3-Round Modified Delphi Process (2 HTR/KT experts, 2 KT

experts, 1 HTR expert)

 Third Round-Application of criteria:

 Face validity (KT theories, models, or frameworks that are common and well-known should be

included)

 Active KT theories, models, or frameworks (passive KT theories, models, or frameworks were

excluded)

 Feasible to apply to take something out of practice  Pragmatic (theoretical KT theorises, models, or frameworks were excluded)  Specific (vague or those that were not prescriptive were excluded)  Could build on other KT theories, models, or frameworks but needed to be generic rather than for

a specific context

 Easily understood and practical

Potential List of 16 KT TMFs for HTR

 Classic Theory=1  Diffusion of Innovations (Rogers, 3rd Edition, 1983)  Frameworks=2  Consolidated Framework for Implementation Research (CFIR) (Damschroder, 2009)  Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) (Glasgow, 1999)  Fits both Model/Framework=1

 Evidence-Driven Community Health Improvement Process (EDCHIP) (Layde, 2012)

 Process Models=12

Potential KT Process Models for HTR

KT Models Stages of Research Evaluation (Nutbeam, 2006) Knowledge-to-Action (KTA) (Graham, 2006) Quality Implementation Framework (Meyers, 2012) Western Australia (WA) Health Network Policy Development and Implementation Cycle (Briggs, 2012) Collaborative Model for Achieving Breakthrough improvement (Institute for Healthcare Improvement, 2003) Healthcare Improvement Collaborative Model (Edward, 2017)

Potential KT Process Models for HTR

KT Models

Co-KT framework (Kitson, 2013) Plan-Do-Study-Act (PDSA) Cycles (Deming, 1986) A Staged Model of Innovation Development and Diffusion of Health Promotion Programs (Oldenburg, 1996) CollaboraKTion framework (Jenkins, 2016) KT framework for Agency for Healthcare Research and Quality (AHRQ) patient safety portfolio and grantees (Nieva, 2005) Design Focused Implementation Model (Ramaswamy, 2018)

Modified Delphi Process

22 International Experts (11 KT and 11 HTR) Countries: Canada, US, UK, Australia, Germany,

Spain, Italy, and Sweden

Round 1: Survey of 16 KT Theories, Models,

Frameworks (Jan to March 2019)

Survey Questions for each KT Theory, Model, Framework

Familiarity Logical Consistency/Plausibility Degree of specificity Accessibility Ease of use HTR Suitability

Next Steps

 Complete analysis of Round 1  Round 2: Key Informant Interviews with experts  Identification of key constructs/attributes/elements of a

KT theory, model, or framework for HTR

Acknowledgments

Supervisors:

• Dr. Fiona Clement
• Dr. Heather Hanson

Committee members:

• Dr. Jayna Holroyd-Leduc
• Dr. Daniel Niven

Rosmin.esmail@ucalgary.ca

Is Health Technology Reassessment Clinically Relevant?

CADTH – April 15, 2019

Objective – Tell You a Clinical Story of HTR

 Patient case to ground us in clinical reality  Share lessons learned

Passive diffusion of knowledge is ineffective Focus on reproducible science Test effectiveness

Passive Diffusion of Knowledge is Ineffective Lesson #1

JAMA Intern Med 2015; 175: 801-09

The Tale of Tight Glycemic Control

Leuven I (2001) NICE-SUGAR (2009)

• Single center RCT
• N = 1,548
• NNT = 29 (survival)
• Multi-center RCT
• N = 6,104
• NNH = 38 (death)

Tight Glycemic Control

Focus on Reproducible Science

Lesson #2

Inconsistent Scientific Findings

Ioannidis JAMA 2005, Prasad et al. Arch Int Med 2011, Prasad et al. Mayo Clinic Proc. 2013

44% 46% 38%

Time to Reproduction of Research

Risk of the Oscillating Science

Test Effectiveness Efficacy is Not Enough

Lesson #3

Critical Care in Alberta

Prevent Blood Clots

Unfractionated Heparin

Low Molecular Weight Heparin

Interventions

Intervention & Process Change

Clinical Effects

Healthcare Utilization Effects

Technology & Science Evolve

 Care will not change on its own  Focus on technologies with

reproducible science

 Test effectiveness – efficacy is

not enough

Acknowledgements

Mentors

 Sharon Straus

Collaborators

 Sean Bagshaw  Fiona Clement  Chip Doig  Kirsten Fiest  Barry Kushner  Dan Niven

 Jeanna Parsons Leigh

 Dan Zuege  Dave Zygun

Trainees

 Kea Archibold  Kyla Brown  Chloe de Grood  Hasham Kamran

Research Team

 Jamie Boyd  Rebecca Brundin-Mather  Andrea Soo

Funding Agencies

 Alberta Innovates  CIHR  NCE

Major barriers for HTR

• 1. Engagement across multiple levels of the healthcare

system

• 2. Difficulty identifying and prioritizing low value care
• 3. Little guidance and/or methods for implementation

(Sevick et al., 2017; Elshaug et al., 2007; Daniels et al., 2013; Rooshenas et al., 2015; Schlesinger and Grob, 2017)

Pick the team to win

Patients, community, civil society

• rganisations
• Present and past patients and the wider public;

represented as individuals or groups (e.g., patient advocacy groups with experience with technology)

Clinical professionals

• Individuals involved in the care of patients and

use of the technology; represented as individuals or in groups by clinical professional associations

Industry representatives

• Includes technology manufacturers,

pharmaceutical industry, and industry union

System leaders

• Administrators and executives in arm's-length (e.g.,

safety and health quality commissions) or non- government organisations, and third party payers or insurers

Government policy-makers

• Elected officials (e..g., Ministers of Health) at

the regional (e.g., municipal, provincial, state)

• r federal levels

Academic and other researchers

• With expertise in health technology

assessment, health economics, health services research, epiemiology, implementation science

Understand the playing field

What is the role of the government and other third party payer/s? Who is covered and how is it financed including any rules/limits? How is the delivery system organized and financed? What important political forces or issues need to be considered? What assets are at your disposal?

 Health data sources  Human resources  Funding

A Data-Driven Prioritization Process

TECHNOLOGY SELECTION

Identification Prioritization

1

• Data-driven
• Routine & replicable
• Stakeholder collaboration
• Actionable
• High return on investment

• In-hospital admissions (DAD)
• Physician claims
• Laboratory data

Pilot tested in British Columbia

Final reflections